11-MINUTE READ · By Supermetrics.

We’re happy to announce a major update to our Google Search Console integration for both Google Sheets and Google Data Studio. We are adding several new fields which make it much easier to analyze Search Console data. Big thanks to Joona Tuunanen and Jyri Vuorinen from the OIKIO agency, who provided the inspiration for doing this update.

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Branded vs. non-branded search queries

Creating CTR curves easily

Analyzing landing page performance by directory

Analyzing long-tail search queries

Data precision

Coming soon: fetching more than 90 days

Branded vs. non-branded search queries

When analysing your search traffic, it usually makes sense to look at branded and non-branded search queries separately. With our new branded vs. non-branded search queries dimension, you can easily distinguish these two groups of queries from each other.

This dimension would help you acquire a great deal of useful insights: for example, you can easily compare the amount of branded vs. non-branded traffic or see what amount of organic traffic was brought by branded keywords. A graph like this helps you easily see the how the CTR for branded vs. non-branded keywords has changed over time:

Using the branded vs. non-branded search queries dimension

In our Google Sheets add-on, you can specify the brand terms for your company in the Options tab. In Google Data Studio, you can do that in the config screen for the data source. If you don’t specify any brand terms, we use your domain name as the brand term (eg. when fetching Search Console data for “https://supermetrics.com”, we assume “supermetrics” is the brand). Our system will automatically link close variants of your brand terms with your brand, eg. “supermetrix” will be considered a brand term for Supermetrics.

Creating CTR curves easily

CTR curves that plot click-through rates by search engine result position are tremendously helpful for many things. For instance, you can compare your CTRs to industry averages to see how your SERP listings are performing, measure the impact of changes you make to title tags, meta descriptions and rich results, and estimate what effect a change in rank could have in terms of clicks.

Previously, creating these graphs required a lot of spreadsheet data processing, but now you can get them very easily by using our two new dimensions, “SERP position (rounded avg.)” and “SERP position (rounded avg., paged)”. The former will output each position separately, while the latter will output positions 1 to 10 separately and further positions in groups of ten.

Analyzing landing page performance by directory

Google Search Console makes it simple to analyze performance by landing page, but if you want to see the performance of a group of pages, for example everything under “/blog/”, you will need to do a lot of work in a spreadsheet to categorize the data. Even when analyzing a single page, you may see that Search Console lists it multiple times with different query parameters or anchors (eg. “/product” and “/product?sku=858”), so you need to combine them in a spreadsheet to get the overall stats for the page.

Our connector now makes it very easy to analyze landing pages on whatever level you want. You can split the data by these landing page dimensions:

  • Full URL
  • Protocol (http or https)
  • Hostname
  • Path
  • Path directory levels 1 to 4
  • Query parameters
  • Anchor

With those new dimensions you can break the URLs down to multiple parts or extract the relevant part of the URL in a few clicks. Let’s take a look at the URL example below to see which dimension extracts which part of the URL:

You can extract different parts of the URL to group data and analyze it in a number of different scenarios. Below we will cover some of them. 

For example, the “hostname” dimension could help you easily separate the regional versions of your site – you can compare your blog traffic for a specific article in two different countries.

Directory levels allow you to easily group different pages together: for instance, you can pull data for posts on a particular topic (“seo” in the example above).

Query parameters help you drill down into the page URL even further. E.g. you can easily extract and group pages from different properties, traffic to which was brought by affiliates based on the affiliate parameter in the URL.

The new update allows to go even more granular: it can pull anchor data, a specific section of the page traffic was brought to.

Analyzing long-tail search queries

It is widely known that search queries containing long-tail queries are more specific –

We’ve added a new “# of words in search query” dimension. This makes it easy to identify long-tail search queries with untapped potential, as these usually have several words.

Short-tail or generic keywords used in search typically contain 1 – 3 words and long tail terms contain 4 or more. You can build a chart which shows the CTR of a specific position for keywords of the different length to identify your visitors’ search behavior. Do they use more specific phrases to find you or is it just one keyword? What is the SERP position of your long-term keywords?

Data precision

For highly-trafficked sites, getting fully accurate data may require the Supermetrics system to make a large number of Search Console API requests. In many cases, to ensure accuracy, we need to split the date range you are fetching into many smaller pieces and then stitch those results together in our system. The drawback of this approach is that pulling data might take some time.

To make the process smoother and faster, we are introducing a new setting for controlling the data precision. This is available in the Options tab in our Google Sheets add-on, and in the config view of our Google Data Studio connector. The setting has four options for data precision:

  • Normal: we don’t do anything special to improve precision, we make a single request to the Search Console API to fetch your full date range. This is fast but the results may not include data for all long-tail search queries.
  • Enhanced: we split your date range into batches of 11 days
  • High: We split your date range into batches of 6 days
  • Very high: We split your date range into batched of 2 days

The last two options may be very slow to run, but should result in more accurate data, especially for highly-trafficked sites. Due to the load they place on our system, these are only available on our enterprise packages; if you are interested, please contact sales@supermetrics.com.

Coming soon: fetching more than 90 days

Google is working on allowing 3rd party tools like Supermetrics to fetch more than 90 days of Search Console data. This will hopefully become available during the next couple of months. Meanwhile, you can use our “Combine new results with old” option to gather longer time series of data.

Your opinion matters

We added these new dimensions to help you better analyze your GSC data. At Supermetrics, we take user feedback seriously. And we’re constantly adding new features and integrations based on the feedback to help you better report, analyze and monitor your marketing data in one place. If you have ideas how we can improve our products, please let us know and your feedback would be highly appreciated at info@supermetrics.com



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